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#MIT Schwarzman College of Computing

  • A philosophy of work

    10 Avr
  • New technique makes AI models leaner and faster while they’re still learning

    10 Avr
  • Helping data centers deliver higher performance with less hardware

    10 Avr
  • Evaluating the ethics of autonomous systems

    10 Avr
  • Preview tool helps makers visualize 3D-printed objects

    10 Avr
  • AI system learns to keep warehouse robot traffic running smoothly

    10 Avr
  • Augmenting citizen science with computer vision for fish monitoring

    10 Avr
  • MIT and Hasso Plattner Institute establish collaborative hub for AI and creativity

    10 Avr
  • A better method for identifying overconfident large language models

    10 Avr
  • Generative AI improves a wireless vision system that sees through obstructions

    10 Avr
  • MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

    10 Avr
  • Can AI help predict which heart-failure patients will worsen within a year?

    10 Avr
  • 3 Questions: On the future of AI and the mathematical and physical sciences

    10 Avr
  • New MIT class uses anthropology to improve chatbots

    10 Avr
  • A better method for planning complex visual tasks

    10 Avr
  • Improving AI models’ ability to explain their predictions

    10 Avr
  • A “ChatGPT for spreadsheets” helps solve difficult engineering challenges faster

    10 Avr
  • New method could increase LLM training efficiency

    10 Avr
  • Mixing generative AI with physics to create personal items that work in the real world

    10 Avr
  • AI to help researchers see the bigger picture in cell biology

    10 Avr
  • Enhancing maritime cybersecurity with technology and policy

    10 Avr
  • Parking-aware navigation system could prevent frustration and emissions

    10 Avr
  • Personalization features can make LLMs more agreeable

    10 Avr
  • Study: Platforms that rank the latest LLMs can be unreliable

    10 Avr
  • Helping AI agents search to get the best results out of large language models

    10 Avr
  • Brian Hedden named co-associate dean of Social and Ethical Responsibilities of Computing

    10 Avr
  • Antonio Torralba, three MIT alumni named 2025 ACM fellows

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  • Katie Spivakovsky wins 2026 Churchill Scholarship

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  • The philosophical puzzle of rational artificial intelligence

    10 Avr
  • Why it’s critical to move beyond overly aggregated machine-learning metrics

    10 Avr
  • At MIT, a continued commitment to understanding intelligence

    10 Avr
  • Generative AI tool helps 3D print personal items that sustain daily use

    10 Avr
  • 3 Questions: How AI could optimize the power grid

    10 Avr
  • MIT scientists investigate memorization risk in the age of clinical AI

    10 Avr
  • MIT in the media: 2025 in review

    10 Avr
  • Guided learning lets “untrainable” neural networks realize their potential

    10 Avr
  • A new way to increase the capabilities of large language models

    10 Avr
  • 3 Questions: Using computation to study the world’s best single-celled chemists

    10 Avr
  • Enabling small language models to solve complex reasoning tasks

    10 Avr
  • New MIT program to train military leaders for the AI age

    10 Avr
  • New method improves the reliability of statistical estimations

    10 Avr
  • New materials could boost the energy efficiency of microelectronics

    10 Avr
  • MIT affiliates named 2025 Schmidt Sciences AI2050 Fellows

    10 Avr
  • MIT researchers “speak objects into existence” using AI and robotics

    10 Avr
  • A smarter way for large language models to think about hard problems

    10 Avr
  • MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

    10 Avr
  • New control system teaches soft robots the art of staying safe

    10 Avr
  • MIT Sea Grant students explore the intersection of technology and offshore aquaculture in Norway

    10 Avr
  • Researchers discover a shortcoming that makes LLMs less reliable

    10 Avr
  • MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

    10 Avr
  • Understanding the nuances of human-like intelligence

    10 Avr
  • Charting the future of AI, from safer answers to faster thinking

    10 Avr
  • MIT researchers propose a new model for legible, modular software

    10 Avr
  • Teaching robots to map large environments

    10 Avr
  • 3 Questions: How AI is helping us monitor and support vulnerable ecosystems

    10 Avr
  • A faster problem-solving tool that guarantees feasibility

    10 Avr
  • Five with MIT ties elected to National Academy of Medicine for 2025

    10 Avr
  • Creating AI that matters

    10 Avr
  • New software designs eco-friendly clothing that can reassemble into new items

    10 Avr
  • Method teaches generative AI models to locate personalized objects

    10 Avr
  • Blending neuroscience, AI, and music to create mental health innovations

    10 Avr
  • MIT Schwarzman College of Computing and MBZUAI launch international collaboration to shape the future of AI

    10 Avr
  • Using generative AI to diversify virtual training grounds for robots

    10 Avr
  • Fighting for the health of the planet with AI

    10 Avr
  • New prediction model could improve the reliability of fusion power plants

    10 Avr
  • AI maps how a new antibiotic targets gut bacteria

    10 Avr
  • Responding to the climate impact of generative AI

    10 Avr
  • New AI system could accelerate clinical research

    10 Avr
  • What does the future hold for generative AI?

    10 Avr
  • How to build AI scaling laws for efficient LLM training and budget maximization

    10 Avr
  • Machine-learning tool gives doctors a more detailed 3D picture of fetal health

    10 Avr
  • DoE selects MIT to establish a Center for the Exascale Simulation of Coupled High-Enthalpy Fluid–Solid Interactions

    10 Avr
  • A greener way to 3D print stronger stuff

    10 Avr
  • A new generative AI approach to predicting chemical reactions

    10 Avr
  • 3 Questions: The pros and cons of synthetic data in AI

    10 Avr
  • 3 Questions: On biology and medicine’s “data revolution”

    10 Avr
  • MIT researchers develop AI tool to improve flu vaccine strain selection

    10 Avr
  • Simpler models can outperform deep learning at climate prediction

    10 Avr
  • Can large language models figure out the real world?

    10 Avr
  • A new way to test how well AI systems classify text

    10 Avr
  • Eco-driving measures could significantly reduce vehicle emissions

    10 Avr
  • MIT tool visualizes and edits “physically impossible” objects

    10 Avr
  • New algorithms enable efficient machine learning with symmetric data

    10 Avr
  • Robot, know thyself: New vision-based system teaches machines to understand their bodies

    10 Avr
  • A new way to edit or generate images

    10 Avr
  • The unique, mathematical shortcuts language models use to predict dynamic scenarios

    10 Avr
  • Can AI really code? Study maps the roadblocks to autonomous software engineering

    10 Avr
  • How to more efficiently study complex treatment interactions

    10 Avr
  • Changing the conversation in health care

    10 Avr
  • AI shapes autonomous underwater “gliders”

    10 Avr
  • Study could lead to LLMs that are better at complex reasoning

    10 Avr
  • Using generative AI to help robots jump higher and land safely

    10 Avr
  • LLMs factor in unrelated information when recommending medical treatments

    10 Avr
  • Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event

    10 Avr
  • Unpacking the bias of large language models

    10 Avr
  • A sounding board for strengthening the student experience

    10 Avr
  • Bringing meaning into technology deployment

    10 Avr
  • Photonic processor could streamline 6G wireless signal processing

    10 Avr
  • Inroads to personalized AI trip planning

    10 Avr
  • Melding data, systems, and society

    10 Avr

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